The climate system includes a variety of physical processes, such as cloud processes, radiative processes and boundary-layer processes, which interact with each other on many temporal and spatial scales. Due to the limited resolutions of the models, many of these processes are not resolved adequately by the model grid and must therefore be parametrized. The differences between parametrizations are an important reason why climate model results differ. For example, a new boundary-layer parametrization (Lock et al., 2000; Lock, 2001) had a strong positive impact on the simulations of marine stratocumulus cloud produced by the Geophysical Fluid Dynamics Laboratory (GFDL) and the Hadley Centre climate models, but the same parametrization had less positive impact when implemented in an earlier version of the Hadley Centre model (Martin et al., 2006). Clearly, parametrizations must be understood in the context of their host models.
Cloud processes affect the climate system by regulating the flow of radiation at the top of the atmosphere, by producing precipitation, by accomplishing rapid and sometimes deep redistributions of atmospheric mass and through additional mechanisms too numerous to list here (Arakawa and Schubert, 1974; Arakawa, 2004). Cloud parametrizations are based on physical theories that aim to describe the statistics of the cloud field (e.g., the fractional cloudiness or the area-averaged precipitation rate) without describing the individual cloud elements. In an increasing number of climate models, microphysical parametrizations that represent such processes as cloud particle and raindrop formation are used to predict the distributions of liquid and ice clouds. These parametrizations improve the simulation of the present climate, and affect climate sensitivity (Iacobellis et al., 2003). Realistic parametrizations of cloud processes are a prerequisite for reliable current and future climate simulation (see Section 8.6).
Data from field experiments such as the Global Atmospheric Research Program (GARP) Atlantic Tropical Experiment (GATE, 1974), the Monsoon Experiment (MONEX, 1979), ARM (1993) and the Tropical Ocean Global Atmosphere (TOGA) Coupled Ocean-Atmosphere Response Experiment (COARE, 1993) have been used to test and improve parametrizations of clouds and convection (e.g., Emanuel and Zivkovic-Rothmann, 1999; Sud and Walker, 1999; Bony and Emanuel, 2001). Systematic research such as that conducted by the GCSS (Randall et al., 2003) has been organised to test parametrizations by comparing results with both observations and the results of a cloud-resolving model. These efforts have influenced the development of many of the recent models. For example, the boundary-layer cloud parametrization of Lock et al. (2000) and Lock (2001) was tested via the GCSS. Parametrizations of radiative processes have been improved and tested by comparing results of radiation parametrizations used in AOGCMs with those of much more detailed ‘line-by-line’ radiation codes (Collins et al., 2006). Since the TAR, improvements have been made in several models to the physical coupling between cloud and convection parametrizations, for example, in the Max Planck Institute (MPI) AOGCM using Tompkins (2002), in the IPSL-CM4 AOGCM using Bony and Emanuel (2001) and in the GFDL model using Tiedtke (1993). These are examples of component-level testing.
In parallel with improvement in parametrizations, a non-hydrostatic model has been used for downscaling. A model with a 5 km grid on a domain of 4,000 x 3,000 x 22 km centred over Japan has been run by MRI/JMA, using the time-slice method for the Fourth Assessment Report (AR4) (Yoshizaki et al., 2005).
Aerosols play an important role in the climate system. Interactive aerosol parametrizations are now used in some models (HADGEM1, MIROC-hi, MIROC-med). Both the ‘direct’ and ‘indirect’ aerosol effects (Chapter 2) have been incorporated in some cases (e.g., IPSL-CM4). In addition to sulphates, other types of aerosols such as black and organic carbon, sea salt and mineral dust are being introduced as prognostic variables (Takemura et al., 2005; see Chapter 2). Further details are given in Section 8.2.5.